Update app.py

app.py

@@ -1,15 +1,17 @@
 import gradio as gr
 import torch
+import cv2
+import numpy as np
 from sahi.prediction import ObjectPrediction
 from sahi.utils.cv import visualize_object_predictions, read_image
 from ultralyticsplus import YOLO
 
 def yolov8_inference(
     image: gr.Image = None,
-    model_path: gr.Dropdown = None,
-    image_size: int = 640,  # Set default value here
-    conf_threshold: float = 0.25,  # Set default value here
-    iou_threshold: float = 0.45,  # Set default value here
+    model_path: str = "foduucom/Tyre-Quality-Classification-AI",
+    image_size: int = 640,
+    conf_threshold: float = 0.25,
+    iou_threshold: float = 0.45,
 ):
     """
     YOLOv8 inference function
@@ -21,45 +23,51 @@ def yolov8_inference(
         iou_threshold: IOU threshold
     
     """
+    # Load your model using the specified model_path (You should adjust this part based on your model loading logic)
     model = YOLO(model_path)
     model.overrides['conf'] = conf_threshold
     model.overrides['iou'] = iou_threshold
     model.overrides['agnostic_nms'] = False  # NMS class-agnostic
-    model.overrides['max_det'] = 1000 
-    
-    # observe results
+    model.overrides['max_det'] = 1000
+
+    # Preprocess your image as needed (You should adjust this part based on your preprocessing logic)
+    image_cv = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
+    image_rgb = cv2.cvtColor(image_cv, cv2.COLOR_BGR2RGB)
+
+    # Perform inference with your model (You should adjust this part based on your inference logic)
+    results = model(image_cv)
+
+    # Observe results (You should adjust this part based on your result extraction logic)
     top_class_index = torch.argmax(results[0].probs).item()
     Class1 = model.names[top_class_index]
-    globals(Class1)
-        
 
+    return Class1
+
+# Define Gradio input and output components
 inputs = [
-    gr.Image(type="filepath", label="Input Image"),
-    gr.Dropdown(
-    choices=["foduucom/Tyre-Quality-Classification-AI"], 
-    value="foduucom/Tyre-Quality-Classification-AI", 
-    label="Model"),
-    gr.Slider(minimum=320, maximum=1280, step=32, label="Image Size"),
-    gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label="Confidence Threshold"),
-    gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label="IOU Threshold"),
+    gr.Image(type="file", label="Input Image"),
+    gr.Textbox(label="Model Path", default="foduucom/Tyre-Quality-Classification-AI"),
+    gr.Number(default=640, label="Image Size"),
+    gr.Slider(minimum=0.0, maximum=1.0, default=0.25, label="Confidence Threshold"),
+    gr.Slider(minimum=0.0, maximum=1.0, default=0.45, label="IOU Threshold"),
 ]
 
-outputs = gr.Text(Class1)
+outputs = gr.Textbox(label="Result")
+
 title = "AI-Powered Tire Quality Inspection: YOLOv8s Enhanced Classification"
 
 description = """
 Welcome to our 🤖 AI-Powered Tire Quality Inspection Space – a cutting-edge solution harnessing the capabilities of YOLOv8s to revolutionize 🚗 tire quality control processes.
 """
 
-examples = [['Sample/Bald tyre.jpg', 'foduucom/Tyre-Quality-Classification-AI', 640, 0.25, 0.45], ['Sample/Good tyre.png', 'foduucom/Tyre-Quality-Classification-AI', 640, 0.25, 0.45]]
 demo_app = gr.Interface(
     fn=yolov8_inference,
     inputs=inputs,
     outputs=outputs,
     title=title,
     description=description,
-    examples=examples,
-    cache_examples=True,
     theme='huggingface',
 )
-demo_app.queue().launch(debug=True)
+
+# Launch the Gradio app
+demo_app.launch()